Computing system for assigning maneuver labels to autonomous vehicle sensor data

ABSTRACT

Various technologies described herein pertain to labeling sensor data generated by autonomous vehicles. A computing device identifies candidate path plans for an object in a driving environment of an autonomous vehicle based upon sensor data generated by sensor systems of the autonomous vehicle. The sensor data is indicative of positions of the object in the driving environment at sequential timesteps in a time period. Each candidate path plan is indicative of a possible maneuver being executed by the object during the time period. The computing device generates a weighted directed graph based upon the candidate path plans. The computing device determines a shortest path through the weighted directed graph. The computing device assigns a maneuver label to the sensor data based upon the shortest path, wherein the maneuver label is indicative of a maneuver that the object executes during the time period.

BACKGROUND

An autonomous vehicle is a motorized vehicle that can operate without ahuman driver. An exemplary autonomous vehicle includes a plurality ofsensor systems, such as but not limited to, a lidar sensor system, acamera sensor system, and a radar sensor system, amongst others. Theautonomous vehicle operates based upon sensor data output by the sensorsystems.

As an autonomous vehicle moves about a driving environment, a predictionsystem of the autonomous vehicle generates predictions as to futurepaths of objects (e.g., vehicles, pedestrians, etc.) in the drivingenvironment based upon the sensor data output by the sensor systems. Theautonomous vehicle may base its operation in part on the predictions.For instance, the autonomous vehicle may select maneuvers to executebased upon the predictions.

Several deficiencies are associated with conventional prediction systemsof autonomous vehicles. First, conventional prediction systems tend torely on heuristic approaches in order to generate predictions. Heuristicapproaches require an inordinate amount of hand-tuning in order togenerate accurate predictions. Furthermore, heuristic approaches oftenlead to conflicting predictions when considering edge cases (i.e.,driving scenarios that are unlikely to occur), especially when noisysensor data is utilized. Second, conventional approaches are unable toaccurately assign probabilistic confidence scores to potential maneuversthat may be undertaken by objects in a driving environment of anautonomous vehicle. Third, conventional approaches tend not toexplicitly consider historical sensor data generated by a fleet ofautonomous vehicles.

SUMMARY

The following is a brief summary of subject matter that is described ingreater detail herein. This summary is not intended to be limiting as tothe scope of the claims.

Described herein are various technologies that pertain to labelingsensor data generated by sensor systems of autonomous vehicles. Morespecifically, a computing device that assigns maneuver labels to sensordata indicative of objects in driving environments of autonomousvehicles is described herein. The computing device may generate acomputer-implemented machine learning model based upon the sensor dataand the maneuver labels assigned to the sensor data. An autonomousvehicle may provide second sensor data as input to the machine learningmodel in order to predict a maneuver that an object in a drivingenvironment of the autonomous vehicle is to execute. The autonomousvehicle may then operate based upon the maneuver the object is predictedto execute.

In operation, a computing device receives sensor data generated bysensor systems of an autonomous vehicle. The sensor data is indicativeof positions of an object in a driving environment of the autonomousvehicle at sequential timesteps in a time period. For example, theobject may be a vehicle, a bicycle, or a pedestrian. The computingdevice identifies candidate path plans for the object based upon aposition of the object detected based on the sensor data from the sensorsystems. Each candidate path plan in the candidate path plans isindicative of a possible maneuver being executed by the object duringthe time period. In an example, the candidate path plans may correspondto lanes of a road.

The computing device generates a weighted directed graph based upon arelationship between consecutive candidate path plans. The weighteddirected graph comprises nodes and weighted directed edges connecting atleast a portion of the nodes. Each node in the nodes is assigned to acandidate path plan in the candidate path plans at a sequential timestepin the sequential timesteps. A weighted directed edge in the weighteddirected edges is indicative of a transition relationship between afirst candidate path plan in the candidate path plans at a firstsequential timestep in the sequential timesteps and a second candidatepath plan in the candidate path plans at a second sequential timestep inthe sequential timesteps for the object.

The computing device determines a shortest path through the weighteddirected graph. The computing device assigns a maneuver label to thesensor data based upon the shortest path. Using the shortest path, thecomputing device determines maneuver labels using properties of a(candidate) path plan at a current timestep as well as a relationship ofthe path plan at the current timestep with a next subsequent, distinct(candidate) path plan for the object. The computing device calculates aduration of a path change beginning from a time when the objectexpresses an intention to make the path change and ending when theobject has completed their intention. The computing device may infer theintention of the object from a turn signal (of the object), horizontalvelocity of the object toward and away from a path boundary and pastkinematical behavior of the object. The maneuver label is indicative ofa maneuver that the object executes during the time period. Forinstance, the maneuver label may be straight, left lane change, rightlane change, left turn, right turn, stationary, or unknown.

The computing device executes an operation based upon the maneuver labeland the sensor data. For instance, the computing device may generate acomputer-implemented machine learning model based in part upon thesensor data and the maneuver label assigned to the sensor data. Themachine learning model may be configured to generate predictions ofmaneuvers that are to be undertaken by objects in environments ofautonomous vehicles.

As such, the computing device may cause the machine learning model to beloaded into memory of the autonomous vehicle (or another autonomousvehicle). As the autonomous vehicle operates, the autonomous vehicle mayprovide second sensor data generated by the sensor systems of theautonomous vehicle as input to the machine learning model. The secondsensor data may be indicative of a second object in a second drivingenvironment of the autonomous vehicle. The machine learning model mayoutput an indication of a maneuver that the second object is predictedto execute. The autonomous vehicle may then operate based upon theindication of the maneuver that the second object is predicted toexecute. For instance, the autonomous vehicle may control at least oneof a vehicle propulsion system of the autonomous vehicle, a brakingsystem of the autonomous vehicle, or a steering system of the autonomousvehicle based upon the maneuver that the second object is predicted toexecute.

The above-described technologies present various advantages overconventional prediction systems in autonomous vehicles. First, bygenerating labeled sensor data that is used to generate a machinelearning model, the above-described technologies obviate the need forexcessive hand-tuning associated with a heuristic approach. Second,unlike heuristic approaches, the machine learning model described above(generated from the labeled sensor data) is less susceptible to noisydata than conventional heuristic approaches. Third, by assigning labelsto sensor data generated by a fleet of autonomous vehicles, historicalsensor data generated by the fleet may be more readily utilized ingenerating the machine learning model.

The above summary presents a simplified summary in order to provide abasic understanding of some aspects of the systems and/or methodsdiscussed herein. This summary is not an extensive overview of thesystems and/or methods discussed herein. It is not intended to identifykey/critical elements or to delineate the scope of such systems and/ormethods. Its sole purpose is to present some concepts in a simplifiedform as a prelude to the more detailed description that is presentedlater.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a functional block diagram of an exemplary computingdevice.

FIG. 2 illustrates assigning maneuver labels to sensor data generated byautonomous vehicles.

FIG. 3 illustrates a functional block diagram of an exemplary computingdevice.

FIG. 4 illustrates a functional block diagram of an exemplary autonomousvehicle.

FIG. 5 illustrates a functional block diagram of an exemplary autonomousvehicle.

FIG. 6 illustrates an exemplary computing environment.

FIG. 7 illustrates a weighted directed graph.

FIG. 8 illustrates a shortest path in the weighted directed graphillustrated in FIG. 7.

FIG. 9 illustrates an exemplary driving environment of an autonomousvehicle.

FIG. 10 illustrates an exemplary driving environment of an autonomousvehicle.

FIG. 11 is a flow diagram that illustrates an exemplary methodologyperformed by a computing device for assigning labels to sensor datagenerated by an autonomous vehicle.

FIG. 12 is a flow diagram that illustrates an exemplary methodologyperformed by a computing device for generating a machine learning modelbased upon labeled sensor data.

FIG. 13 is a flow diagram that illustrates an exemplary methodologyperformed by an autonomous vehicle for operating based upon output of amachine learning model.

FIG. 14 illustrates an exemplary computing device.

DETAILED DESCRIPTION

Various technologies pertaining to assigning maneuver labels to sensordata generated by sensor systems of autonomous vehicles are nowdescribed with reference to the drawings, wherein like referencenumerals are used to refer to like elements throughout. In the followingdescription, for purposes of explanation, numerous specific details areset forth in order to provide a thorough understanding of one or moreaspects. It may be evident, however, that such aspect(s) may bepracticed without these specific details. In other instances, well-knownstructures and devices are shown in block diagram form in order tofacilitate describing one or more aspects. Further, it is to beunderstood that functionality that is described as being carried out bycertain system components may be performed by multiple components.Similarly, for instance, a component may be configured to performfunctionality that is described as being carried out by multiplecomponents.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

As used herein, the terms “component” and “system” are intended toencompass computer-readable data storage that is configured withcomputer-executable instructions that cause certain functionality to beperformed when executed by a processor. The computer-executableinstructions may include a routine, a function, or the like. It is alsoto be understood that a component or system may be localized on a singledevice or distributed across several devices. Further, as used herein,the term “exemplary” is intended to mean “serving as an illustration orexample of something.”

Referring now to FIG. 1, an exemplary computing device 100 isillustrated. The computing device 100 comprises a processor 102 andmemory 104, wherein the memory 104 has a maneuver label generationapplication 106 loaded therein. In general, the maneuver labelgeneration application 106 (when executed by the processor 102) isconfigured to assign maneuver labels to sensor data generated by sensorsystems of autonomous vehicles.

The computing device 100 may further include a data store 108. The datastore 108 comprises a plurality of sensor data 110 generated by sensorsystems of a plurality of autonomous vehicles. The plurality of sensordata 110 is indicative of positions of objects in driving environmentsof the plurality of autonomous vehicles during time periods. Forinstance, the objects may include vehicles, bicycles, and pedestrians.

The data store 108 further comprises mapping data 112. The mapping data112 comprises maps of driving environments in which path plans atpositions in the driving environments are marked, wherein the path plansare indicative of possible maneuvers that can be executed by an objectat the positions reflected in one or more of the maps.

With reference now to FIG. 2, an illustration 200 of assigning maneuverlabels to sensor data is illustrated. As will be described in greaterbelow, the maneuver label generation application 106 is configured toassign maneuver labels 202-214 to a plurality of sensor data 216-218generated by autonomous vehicles, namely sensor data 1 216, . . . , andsensor data M 218, where M can be substantially any integer greaterthan 1. The plurality of sensor data 216-218 may be or include theplurality of sensor data 110 and the plurality of sensor data 110 may beor include the plurality of sensor data 216-218.

As shown in FIG. 2, the maneuver labels 202-214 include a straight label202, a left lane change label 204, a right lane change label 206, a leftturn label 208, a right turn label 210, a stationary label 212, and anunknown label 214. The unknown label 214 captures maneuvers executed byobjects that are not captured by the maneuver labels 202-212. In anexample, the maneuver label generation application 106 can assign one ofthe maneuver labels 202-214 to the sensor data 1 216 and one of themaneuver labels 202-214 to the sensor data M 218. Thus, the maneuverlabel generation application 106 generates labeled sensor data.

Turning now to FIG. 3, the exemplary computing device 100 according tovarious embodiments illustrated. As depicted in FIG. 3, the memory 104of the computing device 100 further includes a machine learning modelgeneration application 302. In general, the machine learning modelgeneration application 302 (when executed by the processor 102) isconfigured to generate a computer-implemented machine learning model 306based upon sensor data that has been assigned maneuver labels by themaneuver label generation application 106.

The data store 108 of the computing device 108 comprises labeled sensordata 304. The labeled sensor data 304 comprises a plurality of sensordata (e.g., the plurality of sensor data 110) generated by sensorsystems of a plurality of autonomous vehicles and maneuver labelsassigned to the plurality of sensor data. In an example, the labeledsensor data 304 may include first sensor data that is indicative ofpositions of an object at sequential timesteps in a time period and afirst maneuver label (e.g., straight, right turn, left turn, etc.)assigned to the first sensor data.

The data store 108 may also comprise the computer implemented machinelearning model 306. In general, the machine learning model 306 isconfigured to output an indication of a maneuver that an object in adriving environment of an autonomous vehicle is predicted to executebased upon sensor data generated by sensor systems of the autonomousvehicle. The computing device 100 may generate the machine learningmodel 306 based upon the labeled sensor data 304.

In an embodiment, the machine learning model 306 may be or include anartificial neutral network (ANN), a deep neural network (DNN), arecurrent neural network (RNN), a long short-term memory (LSTM) RNN, ora convolutional neural network (CNN). The machine learning model 306 mayalso be or include a support vector machine (SVM), a Bayesianclassifier, or other suitable classifier. Furthermore, the machinelearning model 306 may be or include a decision tree or a random forest.

In an example, the machine learning model 306 may comprise nodes andedges that couple nodes in the machine learning model 306. Each edge isassigned a learned weight, wherein the learned weight can be learnedusing a supervised or semi-supervised learning procedure. Accordingly,for instance, a learned weight assigned to an edge can be influenced bya plurality of sensor data from a plurality of autonomous vehicles. Themachine learning model 306 may take sensor data generated by anautonomous vehicle as input. The sensor data may be indicative of anobject in a driving environment of the autonomous vehicle. The machinelearning model 306 outputs an indication of a maneuver that the objectis predicted to execute based upon learned weights of the edges and thesensor data.

In an embodiment, the machine learning model 306 may be configured togenerate a probability distribution over possible maneuvers that can beexecuted by an object in a driving environment. For instance, if thepossible maneuvers include a first maneuver and a second maneuver, themachine learning model 306 may output a first probability that theobject is to execute the first maneuver and a second probability thatthe object is to execute the second maneuver.

Turning now to FIG. 4, an autonomous vehicle 400 is illustrated. Theautonomous vehicle 400 can navigate about roadways without humanconduction based upon sensor data (i.e., sensor signals) outputted bysensor systems of the autonomous vehicle 400. The autonomous vehicle 400includes sensor systems, namely, a sensor system 1 402, . . . , and asensor system N 404, where N can be substantially any integer greaterthan 1 (collectively referred to herein as sensor systems 402-404). Thesensor systems 402-404 are of different types and are arranged about theautonomous vehicle 400. For example, the sensor system 1 402 may be alidar sensor system and the sensor system N 404 may be a camera sensor(image) system. Other exemplary sensor systems included in the sensorsystems 402-404 can include radar sensor systems, satellite-based radionavigation sensor systems (e.g., global positioning system (GPS) sensorsystems), sonar sensor systems, infrared sensor systems, and the like.The sensor systems 402-404 generate (i.e., output) sensor data. Forinstance, the radar sensor systems can generate radar sensor data, thelidar sensor systems can generate lidar sensor data, the camera sensorsystems can generate camera sensor data, etc.

The autonomous vehicle 400 further includes several mechanical systemsthat are used to effectuate appropriate motion of the autonomous vehicle400. For instance, the mechanical systems can include, but are notlimited to, a vehicle propulsion system 406, a braking system 408, and asteering system 410 (collectively, “the mechanical systems 406-410”).The vehicle propulsion system 406 may be an electric motor, an internalcombustion engine, or a combination thereof. The braking system 408 caninclude an engine brake, brake pads, actuators, and/or any othersuitable componentry that is configured to assist in decelerating theautonomous vehicle 400. The steering system 410 includes suitablecomponentry that is configured to control the direction of movement ofthe autonomous vehicle 400.

The autonomous vehicle 400 further comprises a computing system 412. Thecomputing system 412 comprises a processor 414 and memory 416. Thememory 416 may have a perception system 418 and a prediction system 420loaded therein. In general, the perception system 418 (when executed bythe processor 414) is configured to facilitate detection of objects indriving environments of the autonomous vehicle 400. The predictionsystem 420 (when executed by the processor 414) is configured togenerate predictions of future paths of objects in the drivingenvironment of the autonomous vehicle 400.

Referring now to FIG. 5, the autonomous vehicle 400 according to variousembodiments is illustrated. The autonomous vehicle 400 includes thesensor systems 402-404, the mechanical systems 406-410, and thecomputing system 412 (including the processor 414, the memory 416, theperception system 418, and the prediction system 420).

As depicted in FIG. 5, the memory 416 of the autonomous vehicle 400further comprises a computer-implemented machine learning model 502. Inan example, the machine learning model 502 may be or include the machinelearning model 306 described above. The machine learning model 306 mayalso be or include the machine learning model 502. The machine learningmodel 502 may be part of the prediction system 420. The computing system412 of the autonomous vehicle 400 may provide sensor data generated bythe sensor systems 402-404 as input to the machine learning model 502.The sensor data is indicative of an object in a driving environment ofthe autonomous vehicle 400. The machine learning model 502 may thenoutput an indication of a maneuver that the object is predicted toexecute based in part upon the sensor data.

With reference generally now to FIGS. 1-5, operation of the computingdevice 100 and the autonomous vehicle 400 is now set forth. Thecomputing device 100 receives sensor data generated by the sensorsystems 402-404 of the autonomous vehicle 400. The sensor data isindicative of positions of an object in a driving environment of theautonomous vehicle 400 at sequential timesteps in a time period. In anon-limiting example, the object may be a vehicle, such as a car, atruck, or a motorcycle, a bicycle, or a pedestrian. In an embodiment,the time period may range from 1 to 6 seconds. For instance, the timeperiod may range from 1.5 to 5.5 seconds, from 2 to 4 seconds, or from 3to 3.5. The sequential timesteps may occur every 0.05 to 0.2 seconds.For instance, the sequential timesteps may occur every 0.1 to 0.18seconds, every 0.12 to 0.16 seconds, or every 0.13 to 0.15 seconds.

The maneuver label generation application 106 executing on the computingdevice 100 then identifies candidate path plans for the object in thedriving environment of the autonomous vehicle 400 based upon a positionof the object detected based on the sensor data from the sensor systems402-404. Each candidate path plan in the candidate path plans isindicative of a possible maneuver being executed by the object duringthe time period. For instance, a first candidate path plan may beexecuting a left turn and a second candidate path plan may be executinga left lane change. In an example, the candidate path plans maycorrespond to lanes of a road.

The maneuver label generation application 106 may identify the candidatepath plans further based upon the mapping data 112. More specifically,the maneuver label generation application 106 may execute a search overthe mapping data 112 based upon the positions of the object indicated bythe sensor data. The search produces search results, wherein the searchresults include the candidate path plans.

The maneuver label generation application 106 generates a weighteddirected graph based upon the candidate path plans. The weighteddirected graph comprises nodes and weighted directed edges connecting atleast a portion of the nodes. Each node in the nodes is assigned to acandidate path plan in the candidate path plans at a sequentialtimestep. A weighted directed edge in the weighted directed edges isindicative of a transition relationship between a first candidate pathplan at a first sequential timestep in the sequential timesteps and asecond candidate path plan at a second sequential timestep in thesequential timesteps for the object.

The maneuver label generation application 106 then determines a shortestpath through the weighted directed graph by applying a shortest pathalgorithm to the weighted directed graph. For instance, the maneuverlabel generation application 106 may apply Dijkstra's algorithm, aBellman-Ford algorithm, or a Floyd-Warshall algorithm to the weighteddirected graph in order to determine the shortest path.

In an embodiment, prior to determining the shortest path through theweighted directed graph, the maneuver label generation application 106may prune the weighted directed graph by removing weighted directededges that fail to exceed a certain weight threshold. The maneuver labelgeneration application 106 may also remove unconnected nodes in theweighted directed graph.

The maneuver label generation application 106 then assigns a maneuverlabel in the maneuver labels 202-214 to the sensor data based upon theshortest path. More specifically, the maneuver label generationapplication 106 may assign the maneuver label using the shortest path bydetermining properties of a (candidate) path plan at a current timestepas well as a relationship of the (candidate) path plan at the currenttimestep with a next subsequent, distinct (candidate) path plan for theobject. The maneuver label generation application 106 calculates aduration of a path change maneuver from a time that begins when theobject expresses the intention (as determined via the sensor data) tomake the path change and ends when the object has finished theirintention (as determined via the sensor data). More specifically, themaneuver label generation application 106 may infer the intention fromturn signals (of the object), horizontal velocity of the object towardand away from a path boundary and past kinematical behavior of theobject. Thus, the maneuver label generation application 106 generateslabeled sensor data, wherein the labeled sensor data comprises thesensor data and the maneuver label.

The computing device 100 (or another computing device) then performs anoperation based upon labeled sensor data (i.e., the maneuver label andthe sensor data). For instance, the machine learning model generationapplication 302 may generate the computer-implemented machine learningmodel 306 (described above) based upon the sensor data and the maneuverlabel assigned to the sensor data. As described above, the machinelearning model 306 is configured to predict maneuvers that objects indriving environments of autonomous vehicles are to perform.

Subsequently, the computing device 100 may cause the machine learningmodel 306 (referred to hereafter as the machine learning model 502) tobe loaded in the memory 416 of the computing system 412 of theautonomous vehicle 400 (or another autonomous vehicle). As theautonomous vehicle 400 operates, the sensor systems 402-404 generatesecond sensor data. The second sensor data may be indicative of a secondobject in the driving environment of the autonomous vehicle 400 (or asecond driving environment of the autonomous vehicle 400). The computingsystem 412 of the autonomous vehicle 400 receives the second sensordata. The computing system 412 provides the second sensor data as inputto the machine learning model 502 described above. The machine learningmodel 502 outputs an indication of a maneuver that the second object ispredicted to execute based upon the second sensor data. In anembodiment, the maneuver that the second object is predicted to executemay occur within a time period that extends 6 to 10 seconds from a timeat which the computing system 412 receives the second sensor data. Forinstance, the time period may extend from 6.5 to 9 seconds, from 7 to8.5 seconds, or from 7.5 to 8 seconds.

The computing system 412 of the autonomous vehicle 400 then controls atleast one of the vehicle propulsion system 406, the braking system 408,or the steering system 410 based upon the indication of the maneuverthat the second object is predicted to execute. More specifically, thecomputing system 412 of the autonomous vehicle 400 may control at leastone of the vehicle propulsion system 406, the braking system 408, or thesteering system 410 to execute a second maneuver based upon theindication of the maneuver that the second object is predicted toexecute. For instance, the second maneuver may be maintaining a straightheading, a left lane change, a right lane change, a left turn, a rightturn, or remaining stationary.

Turning now to FIG. 6, an exemplary computing environment 600 isillustrated. The computing environment 600 includes the computing device100 described above. The computing environment 600 also includes aplurality of autonomous vehicles, namely an autonomous vehicle 1 602, .. . , and an autonomous vehicle P 604, where P can be substantially anyinteger greater than 1. The autonomous vehicle 400 described above maybe included in the plurality of autonomous vehicles 602-604.Furthermore, each autonomous vehicle in the plurality of autonomousvehicles 602-604 may include components similar or identical to thecomponents of the autonomous vehicle 400 described above.

The plurality of autonomous vehicles 602-604 and the computing device100 are in communication via a network 606 (or several networks). As theplurality of autonomous vehicles 602-604 move about drivingenvironments, a plurality of sensor systems of the plurality ofautonomous vehicles 602-604 generate a plurality of sensor data. Theplurality of autonomous vehicles 602-604 may transmit the plurality ofsensor data to the computing device 100. The computing device 100 (byway of the maneuver label generation application 106) may then assignmaneuver labels to the sensor data as described above, therebygenerating the labeled sensor data 304. Moreover, the computing device100 (by way of the machine learning model generation application 302)may generate the machine learning model 306 and/or the machine learningmodel 502 based upon the labeled sensor data 304.

Although the above-described process has been described as assigning amaneuver label to a single set of sensor data, it is to be understoodthat the above-described process may be employed many times to labelmany different sets of sensor data generated by the plurality of sensorsystems of the plurality of autonomous vehicles 602-604. Furthermore,although the maneuver label generation application 106 and the machinelearning model generation application 302 are described as executing onthe computing device 100, it is to be understood that the maneuver labelgeneration application 106 and the machine learning model generationapplication 302 may execute on separate computing devices.

Referring now to FIG. 7, an exemplary weighted directed graph 700 isillustrated. The maneuver label generation application 106 describedabove may generate the weighted directed graph 700 based upon candidatepath plans for an object in a driving environment of an autonomousvehicle (e.g., the autonomous vehicle 400). The weighted graph 700represents four sequential timesteps: t₁, t₂, t₃, and t₄. The weighteddirected graph 700 comprises nodes (indicated by circles in FIG. 7) andweighted directed edges (indicated by arrows and w₁ to w₁₂ in FIG. 7).Each node in the nodes is indicative of a candidate path plan of theobject at one of the sequential timesteps t₁ to t₄. For instance, asdepicted in FIG. 7, at timestep t₁, the weighted directed graph 700includes first nodes 702-706: a first node 702 (assigned to a firstcandidate path plan), a second node 704 (assigned to a second candidatepath plan), and a third node 706 (assigned to a third candidate pathplan). The first nodes 702-706 are assigned to a first subset ofcandidate path plans in the candidate path plans. At timestep t₂, theweighted directed graph 700 includes second nodes 708-710: a fourth node708 (assigned to the first candidate path plan) and a fifth node 710(assigned to the second candidate path plan). The second nodes in thenodes are assigned to a second subset of candidate path plans in thecandidate path plans. The weighted edges w₁ to w₅ (i.e., a subset of theweighted edges w₁ to w₁₂) connect at least some of the first nodes702-706 at timestep t₁ to at least some of the second nodes 708-710 attimestep t₂.

Referring now to FIG. 8, a shortest path 802 through the weighteddirected graph 700 is illustrated. More specifically, the shortest path802 is a path from a node at a first timestep (e.g., t₁) to a node at alast timestep (e.g., t₄) such that a sum of the weights along the pathis minimized. The maneuver label generation application 106 describedabove may determine the shortest past 802 by applying a shortest pathalgorithm to the weighted directed graph 700. For instance, the shortestpath algorithm may be Dijkstra's algorithm, a Bellman-Ford algorithm, ora Floyd-Warshall algorithm.

Turning now to FIG. 9, a top-down view of a driving environment 900 isillustrated. As depicted in FIG. 9, the driving environment 900 includesa four-way intersection, the autonomous vehicle 400 described above, anda vehicle 902 (i.e., an object). The autonomous vehicle 400 and thevehicle 902 are approaching the intersection from opposite directions.

It is contemplated that the vehicle 902 has recently moved from a rightlane 904 to a left lane 906 in the driving environment 900. As such, itmay be ambiguous as to whether the vehicle 902 is to execute a left handturn 908, continue a straight heading 910, or execute a (wide) rightturn 912. Using the above-described processes, the autonomous vehicle400 may provide sensor data generated by the sensor systems 402-404 asinput to the machine learning model 502. The machine learning model 502may then output an indication of a maneuver that the vehicle 902 ispredicted to execute. For instance, the machine learning model 502 maybe configured to output a probability distribution over the left handturn 908, the straight heading 910, and the right turn 912. Theautonomous vehicle 400 may then operate based upon the indication of themaneuver that the vehicle 902 is predicted to execute.

With reference now to FIG. 10, a top-down view of a driving environment1000 is illustrated. As depicted in FIG. 10, the driving environment1000 includes a two-lane road, the autonomous vehicle 400 describedabove, and a vehicle 1002 (i.e., an object). The two-lane road includesa right lane 1004 and a left lane 1006.

It is contemplated that the vehicle 1002 has recently moved from theright lane 1004 to the left lane 1006 in the driving environment 1000.As such, it may be ambiguous as to whether the vehicle 1002 is tocontinue a straight heading 1008 or execute a right lane change 1010.Using the above-described processes, the autonomous vehicle 400 mayprovide sensor data generated by the sensor systems 402-404 as input tothe machine learning model 502. The machine learning model 502 may thenoutput an indication of a maneuver that the vehicle 1002 is predicted toexecute. For instance, the machine learning model 502 may be configuredto output a probability distribution over the straight heading 1008 andthe right lane change 1010. The autonomous vehicle 400 may then operatebased upon the indication of the maneuver that the vehicle 1002 ispredicted to execute.

FIGS. 11-13 illustrate exemplary methodologies relating to assigningmaneuver labels to sensor data generated by sensor systems of autonomousvehicles. While the methodologies are shown and described as being aseries of acts that are performed in a sequence, it is to be understoodand appreciated that the methodologies are not limited by the order ofthe sequence. For example, some acts can occur in a different order thanwhat is described herein. In addition, an act can occur concurrentlywith another act. Further, in some instances, not all acts may berequired to implement a methodology described herein.

Moreover, the acts described herein may be computer-executableinstructions that can be implemented by one or more processors and/orstored on a computer-readable medium or media. The computer-executableinstructions can include a routine, a sub-routine, programs, a thread ofexecution, and/or the like. Still further, results of acts of themethodologies can be stored in a computer-readable medium, displayed ona display device, and/or the like.

With reference to FIG. 11, a methodology 1100 performed by a computingdevice for assigning maneuver labels to sensor data generated by anautonomous vehicle is illustrated. The methodology 1100 begins at 1102,and at 1104, the computing device identifies candidate path plans for anobject in a driving environment of the autonomous vehicle based uponsensor data generated by sensor systems of the autonomous vehicle. Thesensor data is indicative of positions of the object in the drivingenvironment at sequential timesteps in a time period. Each candidatepath plan is indicative of a possible maneuver being executed by theobject during the time period.

At 1106, the computing device generates a weighted directed graph basedupon the candidate path plans. The weighted directed graph comprisesnodes and weighted directed edges connecting at least a portion of thenodes. Each node in the nodes is assigned to a candidate path plan inthe candidate path plans at a sequential timestep. A weighted directededge in the weighted directed edges is indicative of a transitionrelationship between a first candidate path plan in the candidate pathplans at a first sequential timestep in the sequential timesteps and asecond candidate path plan in the candidate path plans at a secondsequential timestep in the sequential timesteps for the object.

At 1108, the computing device determines a shortest path through theweighted directed graph by applying a shortest path algorithm to theweighted directed graph. At 1110, the computing device assigns amaneuver label to the sensor data based upon the shortest path. Themaneuver label is indicative of a maneuver the object executes duringthe time period. At 1112, the computing device performs an operationbased upon the maneuver label and the sensor data. The methodology 1100concludes at 1114.

Turning to FIG. 12, a methodology 1200 performed by a computing devicefor generating a computer-implemented machine learning model based uponlabeled sensor data is illustrated. The methodology 1200 begins at 1202,and at 1204, the computing device receives labeled sensor data. Thelabeled sensor data comprises a plurality of sensor data generated by aplurality of sensor systems of a plurality of autonomous vehicles andmaneuver labels assigned to the plurality of sensor data. At 1206, thecomputing device generates the computer-implemented machine learningmodel based upon the labeled sensor data. The computer-implementedmachine learning model takes sensor data generated by sensor systems ofan autonomous vehicle as input. The sensor data is indicative of anobject in a driving environment of the autonomous vehicle. Thecomputer-implemented machine learning model is configured to output anindication of a maneuver that the object is predicted to execute. Themethodology 1200 concludes at 1208.

With reference to FIG. 13, a methodology 1300 performed by an autonomousvehicle for navigating based upon output of a computer-implementedmachine learning model is illustrated. The methodology 1300 begins at1302, and at 1304, the autonomous vehicle receives sensor data generatedby sensor systems of the autonomous vehicle. The sensor data isindicative of an object in a driving environment of the autonomousvehicle.

At 1306, the autonomous vehicle provides the sensor data to acomputer-implemented machine learning model. The computer-implementedmachine learning model has been generated based upon a plurality ofsensor data generated by a plurality of autonomous vehicles and maneuverlabels that have been assigned to the plurality of sensor data. Acomputing device has assigned the maneuver labels to the plurality ofsensor data by identifying candidate path plans for objects in drivingenvironments of the plurality of autonomous vehicles, generatingweighted directed graphs based upon the candidate path plans, anddetermining shortest paths through the weighted directed graphs. Thecomputer-implemented machine learning model outputs an indication of amaneuver that the object is predicted to execute.

At 1308, the autonomous vehicle controls at least one of a vehiclepropulsion system, a braking system, or a steering system of theautonomous vehicle based upon the indication of the maneuver that theobject is predicted to execute. The methodology 1300 concludes at 1310.

Referring now to FIG. 14, a high-level illustration of an exemplarycomputing device 1400 that can be used in accordance with the systemsand methodologies disclosed herein is illustrated. For instance, thecomputing device 1400 may be or include the computing device 100 or thecomputing system 412. The computing device 1400 includes at least oneprocessor 1402 that executes instructions that are stored in a memory1404. The instructions may be, for instance, instructions forimplementing functionality described as being carried out by one or moresystems discussed above or instructions for implementing one or more ofthe methods described above. The processor 1402 may be a graphicsprocessing unit (GPU), a plurality of GPUs, a central processing unit(CPU), a plurality of CPUs, a multi-core processor, etc. The processor1402 may access the memory 1404 by way of a system bus 1406. In additionto storing executable instructions, the memory 1404 may also storecomputer-implemented machine learning models, sensor data, labeledsensor data, mapping data, weighted directed graphs, and so forth.

The computing device 1400 additionally includes a data store 1408 thatis accessible by the processor 1402 by way of the system bus 1406. Thedata store 1208 may include executable instructions,computer-implemented machine learning models, sensor data, labeledsensor data, mapping data, weighted directed graphs, etc. The computingdevice 1400 also includes an input interface 1410 that allows externaldevices to communicate with the computing device 1400. For instance, theinput interface 1410 may be used to receive instructions from anexternal computer device, etc. The computing device 1400 also includesan output interface 1412 that interfaces the computing device 1400 withone or more external devices. For example, the computing device 1400 maytransmit control signals to the vehicle propulsion system 406, thebraking system 408, and/or the steering system 410 by way of the outputinterface 1412.

Additionally, while illustrated as a single system, it is to beunderstood that the computing device 1400 may be a distributed system.Thus, for instance, several devices may be in communication by way of anetwork connection and may collectively perform tasks described as beingperformed by the computing device 1400.

Various functions described herein can be implemented in hardware,software, or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable medium. Computer-readablemedia includes computer-readable storage media. A computer-readablestorage media can be any available storage media that can be accessed bya computer. By way of example, and not limitation, suchcomputer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM orother optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Disk and disc, as used herein, includecompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and blu-ray disc (BD), where disks usually reproducedata magnetically and discs usually reproduce data optically withlasers. Further, a propagated signal is not included within the scope ofcomputer-readable storage media. Computer-readable media also includescommunication media including any medium that facilitates transfer of acomputer program from one place to another. A connection, for instance,can be a communication medium. For example, if the software istransmitted from a website, server, or other remote source using acoaxial cable, fiber optic cable, twisted pair, digital subscriber line(DSL), or wireless technologies such as infrared, radio, and microwave,then the coaxial cable, fiber optic cable, twisted pair, DSL, orwireless technologies such as infrared, radio and microwave are includedin the definition of communication medium. Combinations of the aboveshould also be included within the scope of computer-readable media.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (ASICs),Application-specific Standard Products (ASSPs), System-on-a-chip systems(SOCs), Complex Programmable Logic Devices (CPLDs), etc.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable modification and alteration of the above devices ormethodologies for purposes of describing the aforementioned aspects, butone of ordinary skill in the art can recognize that many furthermodifications and permutations of various aspects are possible.Accordingly, the described aspects are intended to embrace all suchalterations, modifications, and variations that fall within the scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the details description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. A computing device comprises: a processor; andmemory that stores computer-readable instructions that, when executed bythe processor, cause the processor to perform acts comprising:identifying candidate path plans for an object in a driving environmentof an autonomous vehicle based upon sensor data generated by sensorsystems of the autonomous vehicle, wherein the sensor data is indicativeof positions of the object in the driving environment at sequentialtimesteps in a time period, wherein each candidate path plan in thecandidate path plans is indicative of a possible maneuver being executedby the object during the time period; generating a weighted directedgraph based upon the candidate path plans, wherein the weighted directedgraph comprises nodes and weighted directed edges connecting at least aportion of the nodes, wherein each node in the nodes is assigned to acandidate path plan in the candidate path plans at a sequential timestepin the sequential timesteps, wherein a weighted directed edge in theweighted directed edges is indicative of a transition relationshipbetween a first candidate path plan in the candidate path plans at afirst sequential timestep in the sequential timesteps and a secondcandidate path plan in the candidate path plans at a second sequentialtimestep in the sequential timesteps for the object; determining ashortest path through the weighted directed graph; assigning a maneuverlabel to the sensor data based upon the shortest path, wherein themaneuver label is indicative of a maneuver that the object executesduring the time period; and generating a computer-implemented machinelearning model based in part upon the sensor data and the maneuver labelassigned to the sensor data, wherein the computer-implemented machinelearning model is configured to predict a second maneuver that a secondobject in a second driving environment of a second autonomous vehicle isto execute based upon second sensor data generated by second sensorsystems of the second autonomous vehicle.
 2. The computing device ofclaim 1, wherein the maneuver label is one of: straight; left lanechange; right lane change; left turn; right turn; stationary; orunknown.
 3. The computing device of claim 1, wherein thecomputer-implemented machine learning model is one of: an artificialneural network (ANN); a deep neural network (DNN); a recurrent neuralnetwork (RNN); a decision tree; a random forest; a support vectormachine (SVM); or a convolutional neural network (CNN).
 4. The computingdevice of claim 1, wherein the computer-implemented machine learningmodel generates a probability distribution over second possiblemaneuvers executed by the second object, wherein the second maneuver isincluded in the second possible maneuvers.
 5. The computing device ofclaim 1, wherein the object is one of: a vehicle; a bicycle; or apedestrian.
 6. The computing device of claim 1, wherein identifying thecandidate path plans comprises executing a search over mapping dataretained in a data store of the autonomous vehicle, wherein the mappingdata comprises path plans for the driving environment, wherein thesearch produces search results including the candidate path plans. 7.The computing device of claim 1, wherein the sensor data is at least oneof radar sensor data, lidar sensor data, or camera sensor data.
 8. Thecomputing device of claim 1, wherein the time period ranges from 1 to 6seconds, wherein the sequential timesteps occur every 0.05 to 0.2seconds within the time period.
 9. A method executed by a processor of aserver computing device, the method comprising: identifying candidatepath plans for an object in a driving environment of an autonomousvehicle based upon sensor data generated by sensor systems of theautonomous vehicle, wherein the sensor data is indicative of positionsof the object in the driving environment at sequential timesteps in atime period, wherein each candidate path plan in the candidate pathplans is indicative of a possible maneuver being executed by the objectduring the time period; generating a weighted directed graph based uponthe candidate path plans, wherein the weighted directed graph comprisesnodes and weighted directed edges connecting at least a portion of thenodes, wherein each node in the nodes is assigned to a candidate pathplan in the candidate path plans at a sequential timestep in thesequential timesteps, wherein a weighted directed edge in the weighteddirected edges is indicative of a transition relationship between afirst candidate path plan in the candidate path plans at a firstsequential timestep in the sequential timesteps and a second candidatepath plan in the candidate path plans at a second sequential timestep inthe sequential timesteps for the object; determining a shortest paththrough the weighted directed graph by applying a shortest pathalgorithm to the weighted directed graph; assigning a maneuver label tothe sensor data based upon the shortest path, wherein the maneuver labelis indicative of a maneuver that the object executes during the timeperiod; and generating a computer-implemented machine learning modelbased in part upon the sensor data and the maneuver label assigned tothe sensor data, wherein the computer-implemented machine learning modelis configured to predict a second maneuver that a second object in thedriving environment of the autonomous vehicle is to execute based uponsecond sensor data generated by the sensor systems of the autonomousvehicle.
 10. The method of claim 9, further comprising: prior toidentifying the candidate path plans, receiving the sensor data from theautonomous vehicle via a network.
 11. The method of claim 9, whereinfirst nodes in the nodes are assigned to a first subset of candidatepath plans in the candidate path plans at the first sequential timestepin the sequential timesteps, wherein second nodes in the nodes areassigned to a second subset of candidate path plans in the candidatepath plans at the second sequential timestep in the sequentialtimesteps, wherein a subset of weighted directed edges in the weighteddirected edges connect at least some of the first nodes to at least someof the second nodes.
 12. The method of claim 9, wherein the shortestpath algorithm is one of a Dijkstra's algorithm, a Bellman-Fordalgorithm, or a Floyd-Warshall algorithm.
 13. The autonomous vehicle ofclaim 9, wherein the computer-implemented machine learning modelgenerates a probability distribution over possible maneuvers that can beexecuted by the second object, wherein the second maneuver is includedin the second possible maneuvers.
 14. An autonomous vehicle comprising:sensor systems; a vehicle propulsion system; a braking system; asteering system; and a computing system that is in communication withthe sensor systems, the vehicle propulsion system, the braking system,and the steering system, wherein the computing system comprises: aprocessor; and memory that stores computer-readable instructions that,when executed by the processor, cause the processor to perform actscomprising: receiving sensor data generated by the sensor systems,wherein the sensor data is indicative of an object in a drivingenvironment of the autonomous vehicle; providing the sensor data asinput to a computer-implemented machine learning model, wherein thecomputer-implemented machine learning model has been generated basedupon a plurality of sensor data generated by a plurality of autonomousvehicles and maneuver labels assigned to the plurality of sensor data,wherein a computing device has assigned the maneuver labels to theplurality of sensor data by: identifying candidate path plans forobjects in driving environments of the plurality of autonomous vehicles;generating weighted directed graphs based upon the candidate path plans,wherein each of the weighted directed graphs comprises nodes andweighted directed edges connecting at least a portion of the nodes,wherein each node in the nodes is representative of a candidate pathplan in the candidate path plans, and wherein a weighted directed edgein the weighted directed edges is indicative of a transitionrelationship between a first candidate path plan in the candidate pathplans at a first sequential timestep and a second candidate path plan inthe candidate path plans at a second sequential timestep; determiningshortest paths through the weighted directed graphs; and assigning themaneuver labels based upon the shortest paths; wherein thecomputer-implemented machine learning model outputs an indication of amaneuver that the object is predicted to execute; and controlling atleast one of the vehicle propulsion system, the braking system, or thesteering system based upon the indication of the maneuver that theobject is predicted to execute.
 15. The autonomous vehicle of claim 14,wherein the sensor data includes at least one of radar sensor data,lidar sensor data, or camera sensor data.
 16. The autonomous vehicle ofclaim 14, wherein the object is predicted to execute the maneuver withina time period that extends 6 to 10 seconds from a time at which thecomputing system receives the sensor data.
 17. The autonomous vehicle ofclaim 14, wherein controlling at least one of the vehicle propulsionsystem, the braking system, or the steering system based upon theindication of the maneuver that the object is predicted to executecomprises: controlling at least one of the vehicle propulsion system,the braking system, or the steering system to execute a second maneuver.18. The autonomous vehicle of claim 17, wherein the second maneuver isone of: maintaining a straight heading; a left lane change; a right lanechange; a left turn; a right turn; or remaining stationary.
 19. Theautonomous vehicle of claim 14, wherein the object is one of: a vehicle;a bicycle; or a pedestrian.
 20. The autonomous vehicle of claim 14,wherein the indication of the maneuver that the object is predicted toexecute comprises a probability distribution over possible maneuversthat can be executed by the object, wherein the maneuver that the objectis predicted to execute is included in the possible maneuvers.